The statistics of the natural environment have been characterized to gain insight in the processing of natural stimuli based on the efficient coding hypothesis. Regularities present in these images have been measured and neurons have been shown to reduce the redundancy present in these stimuli. This analysis has revealed that retinal Ganglion cellsʼ properties can be related to the second order dependencies present in natural images. Such analysis has used the convenient assumption that natural image data is isotropic across the visual field, giving up on this assumption has reveal important dependencies reflected by their neuronal coding . Here we consider and quantify precisely the second order dependencies in images of natural environments due to the imaging properties of a model eye. We generated artificial scenes with three-dimensional edge elements and quantified the resulting distributions of orientations by applying the perspective projection onto a sphere. These distributions show a strong influence of the imaging process on the statistics of the input to the visual system. Secondly, image data from a naturalistic virtual environment was obtained. The second order statistics were computed as a function of eccentricity and radial distance from the center of projection. This confirms strong dependencies of the second order statistics on the position across the visual field. Finally, we repeated the analysis to commonly used image databases including the van Hateren database and quantified the second order dependencies as function of the position across the visual field using a new parametrization of the power spectra. We conclude by providing a detailed quantitative analysis of the second order statistical dependencies of the natural input to the visual system and making predictions of the retinal Ganglion cellsʼ profiles as function of their position across the visual field. 1: Tolhurst (1992) 2: Ruderman, Bialek (1994) 3: Rothkopf, Weisswange, Triesch (2009)